




'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


UpperCAmelCase_					=   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
UpperCAmelCase_					=   "    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"





class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir   ,				'models/bert/'   )   )
     __a								=       self.transformer_dir
     shutil.copy(
         os.path.join(UpperCamelCase   ,				'src/transformers/models/bert/modeling_bert.py'   )   ,				os.path.join(self.transformer_dir   ,				'models/bert/modeling_bert.py'   )   ,				)







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       'src/transformers'
     shutil.rmtree(self.transformer_dir   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     Tuple:
     __a								=       comment + f"\nclass {class_name}(nn.Module):\n" + class_code
     if overwrite_result is not None:
       __a								=       comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
     __a								=       black.Mode(target_versions={black.TargetVersion.PYaa}   ,				line_length=119   )
     __a								=       black.format_str(UpperCamelCase   ,				mode=UpperCamelCase   )
     __a								=       os.path.join(self.transformer_dir   ,				'new_code.py'   )
     with open(UpperCamelCase   ,				'w'   ,				newline='\n'   ) as f:
       f.write(UpperCamelCase   )
     if overwrite_result is None:
       self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase   )   ) == 0   )
     else:
       check_copies.is_copy_consistent(f.name   ,				overwrite=UpperCamelCase   )
       with open(UpperCamelCase   ,				'r'   ) as f:
         self.assertTrue(f.read()   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'   )
     self.assertEqual(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:
     # Base copy consistency
     self.check_copy_consistency(
         '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'   ,				'BertLMPredictionHead'   ,				REFERENCE_CODE + '\n'   ,				)

     # With no empty line at the end
     self.check_copy_consistency(
         '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'   ,				'BertLMPredictionHead'   ,				UpperCamelCase   ,				)

     # Copy consistency with rename
     self.check_copy_consistency(
         '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'   ,				'TestModelLMPredictionHead'   ,				re.sub('Bert'   ,				'TestModel'   ,				UpperCamelCase   )   ,				)

     # Copy consistency with a really long name
     __a								=       'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
     self.check_copy_consistency(
         f"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"   ,				f"{long_class_name}LMPredictionHead"   ,				re.sub('Bert'   ,				UpperCamelCase   ,				UpperCamelCase   )   ,				)

     # Copy consistency with overwrite
     self.check_copy_consistency(
         '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'   ,				'TestModelLMPredictionHead'   ,				UpperCamelCase   ,				overwrite_result=re.sub('Bert'   ,				'TestModel'   ,				UpperCamelCase   )   ,				)







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       check_copies.LOCALIZED_READMES['README_zh-hans.md']

     __a								=       (
         '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
         ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
         ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
         ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
         ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
         ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
         ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
         ' method has been applied to compress GPT2 into'
         ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
         ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
         ' Multilingual BERT into'
         ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
         ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
         ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
         ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
         ' Luong, Quoc V. Le, Christopher D. Manning.'
     )
     __a								=       (
         '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
         ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
         ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
         ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
     )
     __a								=       (
         '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
         ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
         ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
         ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
         ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
         ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
         ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
         ' method has been applied to compress GPT2 into'
         ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
         ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
         ' Multilingual BERT into'
         ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
         ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
         ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
         ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
         ' Christopher D. Manning 发布。\n'
     )

     __a						,							__a								=       check_copies.convert_to_localized_md(
         UpperCamelCase   ,				UpperCamelCase   ,				localized_readme['format_model_list']   )

     self.assertFalse(UpperCamelCase   )
     self.assertEqual(UpperCamelCase   ,				UpperCamelCase   )

     __a						,							__a								=       check_copies.convert_to_localized_md(
         UpperCamelCase   ,				UpperCamelCase   ,				localized_readme['format_model_list']   )

     # Check whether the number of models is equal to README.md after conversion.
     self.assertTrue(UpperCamelCase   )

     __a								=       (
         '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
         ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
         ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
         ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
     )
     __a								=       (
         '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
         ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
         ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
         ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
     )
     __a								=       (
         '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
         ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
         ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
         ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
     )

     __a						,							__a								=       check_copies.convert_to_localized_md(
         UpperCamelCase   ,				UpperCamelCase   ,				localized_readme['format_model_list']   )

     # Check if the model link is synchronized.
     self.assertEqual(UpperCamelCase   ,				UpperCamelCase   )









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



